Production Line Defect Tracking

This dataset provides comprehensive tracking of manufacturing defects by product, production line, and batch, including detailed defect types, severity, root causes, corrective actions, and resolution status. It enables in-depth root cause analysis, supports process improvement initiatives, and helps reduce waste and downtime in manufacturing operations. The dataset is ideal for quality control analytics, operational reporting, and continuous improvement programs.

  • opened 19 times
  • last updated 12 Jul 2025
  • by GoMask
The brief that made it

Root cause analysis of recurring defects

Sample rows

preview · 8 of 70 rows · all 19 columns
defect_idstringseveritystringquantity_affectedintegeraction_taken_bystringproduct_idstringproduct_namestringbatch_numberstringproduction_line_idstringproduction_line_namestringdefect_typestringdefect_descriptionstringdetected_atdatetimedetected_bystringroot_causestringprocess_stepstringcorrective_actionstringaction_taken_atdatetimestatusstringcommentsstring
DEF-20240101-0001major6tech_leePRD-001Smart ThermostatBATCH-2401ALINE-01Main AssemblymisalignmentDisplay panel not aligned with casing2024-01-03T09:24:00op_smithFixture calibration errorassemblyRecalibrated fixture and retrained staff2024-01-03T13:12:00resolvedAdditional checks added
DEF-20240101-0002major2sup_alvarezPRD-003LED Desk LampBATCH-2401BLINE-02Electronics Assemblymissing partSwitch button not installed2024-01-04T10:11:00insp_jonesOperator oversightassemblyOperator retrained on standard work2024-01-04T14:30:00closedblank
DEF-20240101-0003minor1blankPRD-002Smart PlugBATCH-2401ALINE-03Final InspectionscratchNoticeable scratch on top cover2024-01-05T08:45:00insp_nguyenMaterial handlinginspectionblankblankopenPossible packaging issue
DEF-20240101-0004minor3eng_kumarPRD-004Portable SpeakerBATCH-2401CLINE-01Main Assemblypaint defectUneven paint finish on edges2024-01-06T11:02:00op_ramosPaint viscosity variationpaintingAdjusted paint mixture2024-01-06T13:25:00resolvedblank
DEF-20240101-0005major4blankPRD-005Wireless MouseBATCH-2401CLINE-04Peripheral LinemisalignmentScroll wheel misaligned2024-01-07T09:51:00insp_chenblankassemblyblankblankunder investigationEscalated to engineering
DEF-20240101-0006critical1qc_martinezPRD-001Smart ThermostatBATCH-2401BLINE-02Electronics Assemblycomponent failureTemperature sensor not responding2024-01-08T12:32:00op_whiteDefective supplier lottestingQuarantined batch, returned to supplier2024-01-08T16:10:00resolvedSupplier notified
DEF-20240101-0007major5sup_alvarezPRD-002Smart PlugBATCH-2401ALINE-03Final Inspectionlabel errorIncorrect voltage label applied2024-01-09T15:17:00insp_jonesLabel stock mix-uppackagingReviewed warehouse label process2024-01-09T17:25:00closedWarehouse to double-check labels
DEF-20240101-0008critical1eng_kumarPRD-003LED Desk LampBATCH-2401BLINE-02Electronics Assemblyshort circuitPCB short detected during QA2024-01-10T08:23:00qa_aliSolder bridge on boardtestingInspected and reworked PCBs2024-01-10T11:00:00resolvedblank

What the 70 rows show

from the 70-row sample

Major severity stands out: mean quantity_affected is 2.9, against 1.7 for the rest.

  • 2median quantity_affected
  • 4statuses
  • 7process steps
  • 15production lines
  • 15production line names
  • 20product names
Mean quantity_affected by severity70 rows
0241.9minor30 rows2.9major31 rows1.0critical9 rows
quantity_affected70 rows, in bands of 1
01530262311433147quantity_affected →

Median 2, from 1 to 7.

action_taken_by45 rows with a value · 25 left blank
  1. qc_martinez7
  2. sup_alvarez6
  3. eng_kumar6
  4. swilliams3
  5. op_white2
  6. lroberts2
  7. jhernandez2
  8. dlee2
  9. jlee2
  10. tech_lee1
19 columns by typefrom the column list below
  • string 16
  • integer 1
  • datetime 2

Columns

19 columns in three groups
blueprint · 19 columns
columntypedescriptionexample
Text 16 columns
defect_idstringUnique identifier for each recorded defect eventuniqueDEF-20240101-0001
product_idstringUnique identifier for the product associated with the defectPRD-001
product_namestringName or description of the productSmart Thermostat
batch_numberstringProduction batch or lot number for traceabilityBATCH-2401A
production_line_idstringIdentifier for the production line where the defect occurredLINE-01
production_line_namestringName or description of the production lineoptionalMain Assembly
defect_typestringCategory or type of defect (e.g., scratch, misalignment, missing part)misalignment
defect_descriptionstringDetailed description of the defect observedoptionalScroll wheel misaligned
detected_bystringIdentifier or name of the operator or inspector who detected the defectoptionalop_smith
root_causestringIdentified root cause of the defect (if determined)optionalOperator oversight
severitystringSeverity level of the defect (e.g., minor, major, critical)minor · major · criticalmajor
process_stepstringProduction process step where the defect was identified (e.g., assembly, painting, packaging)7 steps · optionalassembly
corrective_actionstringDescription of corrective action taken to address the defectoptionalAdjusted paint mixture
action_taken_bystringIdentifier or name of the person responsible for corrective actionoptionaltech_lee
statusstringCurrent status of the defect (e.g., open, under investigation, resolved, closed)open · under investigation · resolved · closedresolved
commentsstringAdditional notes or comments related to the defect or resolution processoptionalAdditional checks added
Numbers 1 column
quantity_affectedintegerNumber of units affected by this defect occurrence1 or more6
Dates and times 2 columns
detected_atdatetimeDate and time when the defect was detected2024-01-03T09:24:00
action_taken_atdatetimeDate and time when corrective action was implementedoptional2024-01-03T13:12:00

Use it for

  • median quantit…270 rowsmean quantity affecte…1.9minor2.9major1.0critic…

    A manufacturing dashboard

    Quantity_affected by severity and a breakdown of action_taken_by. Excel, Power BI or Tableau.

  • Why do the 31 major rows have a mean quantity_affected of 2.9?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable defects with product_id, product_name and batch_number to fill a screen in front of a buyer.

Not quite right?

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blueprint · production-line-defect-tracking

Behind this dataset

Same schema. As many rows as you need.

These 70 rows came out of a blueprint — 19 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with
  • All defects categorized by type
  • Defect rates above 3% escalate to management
  • Batch numbers are mandatory
  • Defects must be linked to resolution steps
Rows
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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
production-line-defect-tracking

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